Remote Sensing of Snow Cover

1. Remote Sensing of Snow Cover with slides from Jeff Dozier, Tom Painter

2. Topics in Remote Sensing of Snow • Optics of Snow and Ice • Remote Sensing Principles • Applications • Operational Remote Sensing

3. Gamma Rays X rays Ultra-violet(UV) Visible (400 - 700nm) Near Infrared (NIR) Infrared (IR) Microwaves Weather radar Television, FM radio Short wave radio The EM Spectrum 10-1nm 1 nm 10-2mm 10-1mm 1 mm 10 mm 100 mm 1 mm 1 cm 10 cm 1 m 102m Violet Blue Green Yellow Orange Red

4. EM Wavelengths for Snow • Snow on the ground • Visible, near infrared, infrared • Microwave • Falling snow • Long microwave, i.e., weather radar • K (l = 1cm) • X (l = 3 cm) • C (l = 5 cm) • S (l = 10 cm)

5. General reflectance curves from Klein, Hall and Riggs, 1998: Hydrological Processes, 12, 1723 - 1744 with sources from Clark et al. (1993); Salisbury and D'Aria (1992, 1994); Salisbury et al. (1994)

6. 0.05 mm 0.2 mm 0.5 mm 1.0 mm 100 80 60 reflectance (%) 40 20 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 wavelength (mm) Snow Spectral Reflectance

7. Different Impacts in Different Regions of the Spectrum • Visible, near-infrared, and infrared • Independent scattering • Weak polarization • Scalar radiative transfer • Penetration near surface only • ~½ m in blue, few mm in NIR and IR • Small dielectric contrast between ice and water • Microwave and millimeter wavelength • Extinction per unit volume • Polarized signal • Vector radiative transfer • Large penetration in dry snow, many m • Effects of microstructure and stratigraphy • Small penetration in wet snow • Large dielectric contrast between ice and water

8. Visible, Near IR, IR

9. Mapping of snow extent • Subpixel problem • “Snow mapping” should estimate fraction of pixel covered • Cloud cover • Visible/near-infrared sensors cannot see through clouds • Active microwave can, at resolution consistent with topography

10. Landsat Thematic Mapper (TM) • 30 m spatial resolution • 185 km FOV • Spectral resolution • 0.45-0.52 μm • 0.52-0.60 μm • 0.63-0.69 μm • 0.76-0.90 μm • 1.55-1.75 μm • 10.4-12.5 μm • 2.08-2.35 μm • 16 day repeat pass

11. AVIRIS spectra

12. Spectra of Mixed Pixels

13. Analysis of Mixed Pixels • Assuming linear mixing, the spectrum of a pixel is the area-weighted average of the spectra of the “end-members” • For all wavelengths l, • Solve for fn

14. Sept 2, 1993(snow in cirques only) Feb 9, 1994(after big winter storm) Apr 14, 1994(snow line 2400-3000 m) Subpixel Resolution Snow Mapping from Landsat Thematic Mapper (Rosenthal & Dozier, Water Resour. Res., 1996)

15. Subpixel Resolution Snow Mapping from AVHRR May 26, 1995 (AVHRR has 1.1 km spatial resolution, 5 spectral bands)

16. 1 2 3 4 5 AVHRR Fractional SCA Algorithm Execute Sub-pixel snow cover algorithm using reflectance Bands 1,2,3 Scene Evaluation: Degree of Cloud Cover over Study Basins Snow Map Algorithm Output: Mixed clouds, high reflective bare ground, and Sub-pixel snow cover Execute Atmospheric Corrections, Conversion to engineering units AVHRR Bands AVHRR (HRPT FORMAT) Pre-Processed at UCSB [NOAA-12,14,16] Thermal Mask Build Thermal Mask Build Cloud Masks using several spectral-based tests Geographic Mask Application of Cloud, Thermal, and Geographic masks to raw AVTREE output Composite Cloud Mask Masked Fractional SCA Map

17. Subpixel Resolution Snow Mapping from AVIRIS (Painter et al., Remote Sens. Environ., 1998)

18. EOS Terra MODIS • Image Earth’s surface every 1 to 2 days • 36 spectral bands covering VIS, NIR, thermal • 1 km spatial resolution (29 bands) • 500 m spatial resolution (5 bands) • 250 m spatial resolution (2 bands) • 2330 km swath

19. snow ice rock/veg Discrimination between Snow and Glacier Ice, Ötztal Alps Landsat TM, Aug 24, 1989

20. Snow Water Equivalent • SWE is usually more relevant than SCA, especially for alpine terrain • Gamma radiation is successful over flat terrain • Passive and active microwave are used • Density, wetness, layers, etc. and vegetation affect radar signal, making problem more difficult

21. SWE from Gamma • There is a natural emission of Gamma from the soil (water and soil matrix) • Measurement of Gamma to estimate soil moisture • Difference in winter Gamma measurement and pre-snow measurement – extinction of Gamma yields SWE • PROBLEM: currently only Airborne measurements (NOAA-NOHRSC)

22. Microwave Wavelengths

23. Frequency Variation for Dialectric Function and Extinction Properties • Variation in dialectric properties of ice and water at microwave wavelengths • Different albedo and penetration depth for wet vs. dry snow, varying with microwave wavelength • NOTE: typically satellite microwave radiation defined by its frequency (and not wavelength)

24. (1) (2) (3) (4) (5) (6) Modeling electromagnetic scattering and absorption Snow Soil

25. SWE and Other Properties derived from SIR-C/X-SAR SIR-C/X-SAR Snow density Snow depth Particle radius Snow depth in cm Grain radius in mm Snow density Estimated Ground measurements

26. Passive Microwave SWE Estimates • Microwave response affected by: • Liquid water content, crystal size and shape, depth and SWE, stratification, snow surface roughness, density, temperature, soil state, moisture, roughness, vegetation cover • Ratio of different wavelengths • Vertically polarized brightness temperature, TB, gradient • Single frequency vertical polarized TB

27. Passive Microwave SWE Estimates • Advantages: • Daily overpass (SSM/I, Nimbus-7 SMMR) • Large coverage areas • Long time series (eg. Cosmos 243 - Russia 1968) • See through clouds, no dependence on the sun (unlike visible or near IR) • Disadvantages • Large pixel size (12.5 – 25 km) • Still problems with vegetation • Maximum SWE & limitations with wet snow

28. Passive Microwave SWE Products

29. Active Microwave Snow Detection • Has been used to estimate binary SCA at 15 - 30 m resolution as compared to air photos • Advantages: • High resolution • Detection characteristics • Disadvantages: • Repeat of 16 days & narrow Swath width, as per TM • Commercial sensor: ERS-I/II (?), RADARSAT

30. Active Microwave SWE Estimation • Snow cover characteristics influence underlying soil temperature, this affects the dielectric constant of soil • Backscatter from soil influenced by dielectric constant and by soil frost penetration depth • Snow cover insulation properties influence backscatter from Bernier et al., 1999: Hydrol. Proc.13: 3041-3051

31. Active Microwave SWE Estimation Thermal snow resistance (R in oCm3s/J) SWE / R Mean snow density (rs in km/m3) Backscattering ratio (swo - sro in dB) Problem: Maximum SWE detectable in order of 400 mm from Bernier et al., 1999: Hydrol. Proc.13: 3041-3051

32. Weather Radar for Snowfall • Ground-based NEXRAD system covers most of the conterminous US, except some alpine areas • Snowfall estimation improves with time of accumulation, not necessarily required for individual storm events like rainfall • Variation in attenuation due to particle shape, wet snow, melting snow • General problems with weather radar

33. Weather Radar vs. Gauge Accumulation from Fassnacht et al., 2001: J. Hydrol. 254: 148-168

34. Particle Characteristics Considerations Mixed precipitation Raw Scaling removed mixed precip + particle shape from Fassnacht et al., 2001: J. Hydrol. 254: 148-168

35. Research / Operational Products • Snow-covered area • Fractional SCA with Landsat or AVHRR (UAz RESAC) • With AVIRIS, also get albedo • Binary SCA currently from MODIS, VIIRS (NPOESS) • Snow-water equivalent • L-band dual polarization + C- and X-band • Daily SSM/I over the Midwest and Prairies • Snow wetness • Near surface with AVIRIS • Within 2% with C-band dual polarization